Adding new pipeline features to existing pipelines

We are an open and inclusive community, welcoming any contributions to pipelines already present in nf-core. In many cases, the original developers might either not have experience with some new fancy method or simply doesn’t have the time to implement everything themselves - so they might be really happy to see you actively contributing!

Basic rules for such contributions:

  • Ask in the Slack channel for the specific pipeline whether there is an open issue on the respective pipeline’s issue tracker for the feature you’re planning to
  • If not, create a new issue there, describing the purpose and ideas you have and wait for someone to comment/discuss
  • If everyone is happy or there is some consensus in the community, start implementing the feature in your fork of the respective pipeline
  • Please do not write to multiple channels in the Slack community, rather collect all of the information in a single GitHub issue, which makes it also much easier to follow up on your proposal

Adding new dependencies to an existing pipeline

Sometimes, especially when adding new features to a pipeline, the dependencies change as well. In such cases, you might want to have an updated Docker Container available before submitting a pull request, in order to have the GitHub Actions tests run through when testing your updated code. To achieve that, please follow these steps:

  • Add only the newly required dependencies to the environment.yml in the pipeline code
  • If you only add new processes to an already existing pipeline however, you can simply specify the container in the nextflow.config file, like so:
process {
    withName:foo {
        container = 'image_name_1'
    withName:bar {
        container = 'image_name_2'
charliecloud {
    enabled = true

An extensive guide on how to handle containers can be found here

  • List this new dependency as something new in the CHANGELOG
  • Create a Pull Request including only these two changes against the dev branch of the pipeline you’re working on

This way, a review process will be very fast and we can merge the changes into the dev branch, updating the Docker Image for that pipeline automatically. After ~30 Minutes, the Docker Image for that pipeline is then updated, and you can open your Pull Request containing your actual pipeline code changes.

Continuous integration testing

To assure that nf-core pipelines don’t break after some change is made to the code, we use automated continuous integration (CI) testing. This is done via GitHub actions, which are defined in the .github/workflows directory. Parameters and file paths are set in the conf/test.config and conf/test_full.config. Please see also here for how to set-up the test workflow for your pipeline.

DSL2 and modules

Nextflow DSL2 allows for a more modularized design of pipelines and the reuse of components. The nf-core team has developed a set of design patterns on how to best implement DSL2 pipelines, which should be used by all nf-core pipelines in order to assure standardization and the reuse of components. The following is meant to help understand certain design choices and how a nf-core DSL2 pipeline should be build.


Each pipeline has a modules directory which contains all the module code. A module here depicts a single process which involves - if possible - only a single tool/software. The modules directory is furthermore divided into localand nf-core sub-directories, where local contains the Modules contained in the local directory are specific to the pipeline, whereas nf-core modules are installed from the nf-core/modules repository. For instance, most pipelines that involve FastQ files will run the FastQC tool for quality control. The module needed for this can be easily reused from the nf-core/modules directory using the nf-core/toolspackage.

For more information and a comprehensive guide on the guidelines of how to implement modules in pipelines please refer to the DSL 2 Modules page

Sample meta information

In nf-core DSL2 pipelines, every channel that contains sample data in some way should also contain a metavariable, which must contain the fields, meta.single_end and meta.strandedness. The meta variable can be passed down to processes as a tuple of the channel containing the actual samples, e.g. FastQ files, and the meta variable. This meta information can easily be extracted from a samplesheet which specifies the input files. Use the nextflow plugin nf-validation to transform the samplesheet into a channel with the meta information.